Network hub centrality and working memory performance in schizophrenia

Cognitive impairment, and working memory deficits in particular, are debilitating, treatment-resistant aspects of schizophrenia. Dysfunction of brain network hubs, putatively related to altered neurodevelopment, is thought to underlie the cognitive symptoms associated with this illness. Here, we used weighted degree, a robust graph theory metric representing the number of weighted connections to a node, to quantify centrality in cortical hubs in 29 patients with schizophrenia and 29 age- and gender-matched healthy controls and identify the critical nodes that underlie working memory performance. In both patients and controls, elevated weighted degree in the default mode network (DMN) was generally associated with poorer performance (accuracy and reaction time). Higher degree in the ventral attention network (VAN) nodes in the right superior temporal cortex was associated with better performance (accuracy) in patients. Degree in several prefrontal and parietal areas was associated with cognitive performance only in patients. In regions that are critical for sustained attention, these correlations were primarily driven by between-network connectivity in patients. Moreover, a cross-validated prediction analysis showed that a linear model using a summary degree score can be used to predict an individual’s working memory accuracy (r = 0.35). Our results suggest that schizophrenia is associated with dysfunctional hubs in the cortical systems supporting internal and external cognition and highlight the importance of topological network analysis in the search of biomarkers for cognitive deficits in schizophrenia.

In order to assess the relationship between key clinical variables and cognitive outcomes, we tested whether clinical measures such as positive symptom severity, antipsychotic medication dose and duration of illness were correlated with working memory performance. Positive symptom severity was assessed via Positive and Negative Syndrome Scale (PANSS) 1 . Antipsychotic medication dose information was available for 23 of the 29 patients included in the current analysis. Chlorpromazine equivalent (CPZE) dose was calculated based on previously validated conversion formulas 2 . Duration of illness was considered from the onset of psychotic symptoms to the time of the experiment. Pearson's correlation coefficients were calculated to assess the correlations between these variables and working memory performance (both average retrieval accuracy and reaction time). The resulting p values were Bonferroni corrected for the six comparisons (3 clinical variables and 2 cognitive outcomes) in this analysis.

Degree-based predictive modeling
In this analysis, we used leave-one-out cross-validation (LOOCV) in our entire sample (N=58) to i) select primary degree features associated with behavior in the training set (N=57), ii) build a summary statistic using these features (i.e., degree score), iii) test the model in the subject that is left out, iv) and evaluate the significance of the prediction using permutation testing. At each iteration of the cross-validation, we removed one subject's data (test set) from the rest of the dataset (training set). We then selected the most relevant features in the training set using a significance threshold (p<0.1) for the correlation between nodal degree and behavior (WM accuracy or reaction time). The selected features were separated into positive and negative sets depending on the sign of the correlation with the behavioral outcome. Next, a summary degree statistic was computed (for each subject in the training set) by summing up the degree values of the nodes in the feature sets. Thus, a separate summary value was obtained for the positive and negative set. We then produced a combined summary score obtained by subtracting the negative set summary score from the positive set score, which utilizes and takes advantage of both feature sets 3 . This metric represented a summary "degree score". The relationship between degree score and the behavioral variable was modeled using linear least squares regression:

BV'= a*DS + b
Where BV stands for behavioral variable (WM accuracy or reaction time) and DS degree score. The models were built for positive and negative sets, as well as the combined set. Finally, the degree score was calculated in the test subject for all three sets, and the behavioral variable was estimated using their respective model. We repeated these steps until each of the 58 participants was used as a test subject. Then, the correlation between the predicted and the observed values of the behavioral variable determined the predictive power of these models. The significance of this correlation was tested via permutation testing. To obtain an empirical null distribution of the correlation, we applied the cross-validated procedure described above using the same degree data and randomly shuffled behavioral scores and repeated this step 1,000 times. Finally, the p-value was calculated as the proportion of permuted correlations that are greater than or equal to the true prediction correlation.

Relationship between clinical and cognitive outcomes
Our analysis of correlation between the main clinical and cognitive outcomes in our study revealed no significant relationship between these sets of variables. The correlations between working memory accuracy and the three clinical variables were not significant: r=-0.17, p=0.37 (PANSS positive), r=0.15, p=0.48 (CPZE), and r=-0.18, p=0.36 (illness duration). Similarly, the correlation between average reaction time and the clinical variables did not reach significance (r=0.24, p=0.21 for PANSS positive, r=-0.08, p=0.72 for CPZE, and r=0.08, p=0.70 for illness duration.

Figure S1
Task. Sternberg Item Recognition Paradigm (SIRP) was used as the working memory task. Each block started with the encoding epoch where participants memorized 1, 3, 5, or 7 consonants. After a brief delay period, single probe letters were presented in succession (14 probes per block). The participants were instructed to press '1' on the keypad if the letter was not in the memorized set (foil) and press '2' if the letter was in the memory set (target). Participants completed 8 blocks (2 per WM load) consisting of 112 trials during a single fMRI run. Analysis pipeline. Resting state functional connectivity was computed among 333 Gordon parcels covering the cerebral cortex for each participant. Subject-level connectivity graphs were defined at varying thresholds (from 2% to 10% edge density in 1% increments) to obtain a robust composite degree value at each node. Group difference. Effect size (Cohen's d) of group differences in nodal degree is shown on the surface maps.
Warm colors indicate nodes with greater degree value in controls, cool colors indicate nodes with greater degree value in patients. Right anterior insula and right postcentral gyrus showed the strongest effects for lower nodal degree in schizophrenia, whereas left intraparietal sulcus showed the largest effect for higher nodal degree in patients.

Effect size, Controls > Patients
Cohen's d

Figure S4
Relationship between nodal degree and behavior across all subjects. Pearson's correlation between weighted degree and each of the WM performance measures is shown on the surface maps. Parahippocampal gyrus and medial default mode network regions showed an inverse relationship between weighted degree and WM performance (i.e., higher degree was associated with poorer performance). Conversely, regions of the ventral attention network showed a positive correlation between nodal degree and WM accuracy.

Figure S5
Relationship between nodal degree and behavior in each group. Pearson's correlation between nodal degree and WM performance measures is shown on the surface maps for controls (top) and schizophrenia patients (bottom). Both groups showed positive correlations between accuracy and nodal degree in superior temporal regions. The degree in default mode regions seemed to correlate more strongly with accuracy in controls, whereas it showed a stronger association with reaction time in patients.

Figure S6
Relationship between nodal degree and behavior in schizophrenia. Scatterplots illustrate the relationships between WM performance and degree in six of the fourteen nodes that showed a significant correlation with performance in patients. See Figure 3